1. The Field of the Invention
This invention relates to signal processing and, more particularly, to novel systems and methods for pattern recognition and data interpretation relative to monitoring and categorizing patterns for predictably quantifying and evaluating systems of an observed entity as they react to stimuli such as, for example, drug profiling.
2. The Background Art
The nervous system is a complex network of tissue for carrying and transmitting signals from one part of a body to another. The nervous system can be divided into the central nervous system and the peripheral nervous system. The central nervous system comprises the brain and spinal cord, to which sensory impulses are transmitted and from which motor impulses pass out, and which coordinates the activity of the entire nervous system. The peripheral nervous system incorporates the remainder of the nerve elements of the body. The autonomic nervous system governs involuntary actions and consists of the sympathetic and the parasympathetic nervous systems.
The impulses (currents) of the nervous system may be measured and charted for the purposes of study and evaluation. For example, electroencephalograms (EEG) can be used to detect and record the impulses of the brain (brain waves). Electrocardiograms (EKG) can be used to gather and chart the impulses and currents of the heart (e.g., changes of electrical potential occurring during the heartbeat). Magnetoencephalographs (MEG) can also be used to measure and chart the changing magnetic fields of the brain. An analysis of the impulses of a particular system or organ (e.g., brain, heart or the like) may provide information as to how the particular observed entity (i.e., human or animal) is performing or reacting to stimuli.
A variety of techniques or strategies of analysis have been developed by those skilled in the art to amplify, analyze and interpret EEG, EKG and MEG waveforms. As appreciated, each of these analysis techniques, however, has its own advantages and disadvantages. Specifically, strategies of analysis used by those skilled in the art may range from the early techniques of spectral analysis and multiple-trial waveform averaging, to various transforms, time-frequency distributions, spatial filtering methods, to one or more of the newer approaches of neural networks, fuzzy logic systems and integrated neurofuzzy systems.
One of the disadvantages of prior art spectral analysis techniques is that they are generally limited to the analysis of a single channel or the comparison of two channels at a time. In addition, spectral analysis usually relies on human inspection of the generated waveforms, whereas in frequency representations, time domain information is implicit or hidden.
Multiple-trial waveform averaging is a widely used analysis technique method that uses summing and averaging over many trials to amplify evoked and event-related signals while reducing background noise. While useful for certain applications, averaging techniques have several significant drawbacks. For example, large quantities of information may be lost in the averaging process as only those signals that are robustly time-locked to a stimulus or response are able to survive the summation over multiple-trials. Another serious disadvantage is that the averaging process only provides a comparison between groups of trials rather than between individual trials themselves. Additionally, the need to first record multiple trials before a reliable evoked potential (EP) can be obtained tends to reduce the utility of signal averaging for real-time applications.
Alternative analysis approaches have been developed by those skilled in the art in an attempt to overcome many of the limitations of multiple-trial waveform averaging. These prior art techniques or methods of analysis may include, for example: (1) Fourier Transforms, (2) Hilbert Transforms, (3) Wavelet Transforms, (4) Short-Time Fourier Transforms, (5) Wigner Functions, (6) Generalized Time-Frequency Distributions and other joint time-frequency distributions. While valuable for certain indications, these alternative prior art approaches are usually accomplished using only a single channel. Therefore, there is no spatial information and, accordingly, inter-channel relationships are often missed. Moreover, these prior art alternate approaches have not been integrated with computerized condition discrimination. Like spectral analysis techniques, these alternate prior art approaches rely on human visual inspection of the generated waveforms in concluding findings, which produces a review process fraught with the potential of observer error.
Spatial filtering methods have also been investigated which include: (1) Principal Component Analysis, (2) Singular Value Decomposition and (3) Eigenvalue Analysis. These prior art filtering methods tend to ignore frequency, and often temporal information as well. Additionally, these prior art spatial analysis techniques must be applied to averaged evoked potentials or the noise level is prohibitive. The foregoing prior art spatial filtering methods therefore are not typically useful for single-trial analysis.
Additional analysis techniques and methodology have been developed by those skilled in the art which take advantage of recent increases in computer processing power. Neural networks have been developed to discover discriminant information. The traditional neural network approaches, however, generally take a long time to program and learn, are difficult to train and tend to focus on local minima to the detriment of other more important areas. Moreover, most of these analysis techniques are limited by a lack of integration with time, frequency and spatial analysis techniques.
Although the forgoing analysis techniques and methods of wave signal processing have provided useful concepts and are valuable in their application in particular areas, due to their inherent narrow ranges of applicability, these prior methods of analysis have provided a fragmentary approach to brain signal evaluation. To this end, the prior art methodologies for evaluating waveforms have significant weaknesses and limitations, and none seem to meet the goals of rapid accurate analysis of all pertinent characteristics of a wide variety of single-trial waveforms. What is needed, therefore, is an integrated waveform analysis method capable of extracting useful information from highly complex and irregular waveforms such as EEG, EKG and MEG data.
In view of the foregoing, it is a primary object of the present invention to provide novel systems and methods for signal processing, pattern recognition and data interpretation by means of observing the affects of a particular state or event on an observed entity.
It is also an object of the present invention to provide a method for improved drug modeling for evaluating the benefits of drugs and side-effect predication in relation to an observed entity (e.g., human or animal).
It is a further object of the present invention to provide an improved method for drug fingerprinting.
Additionally, it is an object of the present invention to provide novel systems and methods for measuring the effect of a particular event or state on the cognitive skills, motor ability, sensation, perception and the like of an observed entity (e.g., human or animal).
It is still a further object of the present invention to provide novel systems and methods for one or more of the following: (1) determining whether a drug successfully crosses the blood-brain barrier; (2) determining whether a drug alters brain function; (3) determining whether a drug modifies cardiovascular activity; (4) determining of dose response relationships by analyzing the effect of a range of doses on EKG or EEG; (5) measuring drug-induced brain activity patterns indicating the presence of particular side effects inducing drowsiness, nausea, headaches, dizziness or cognitive impairment; (6) identifying the effect of a neurological drug on the electrical activity of the brain and heart in animal models and in human clinical trials; (7) improving the accuracy of drug evaluation with improved discrimination of physiological similarities and differences between distinct drug types; (8) lowering the cost of toxicology testing by more accurately revealing the effects of drugs on neurological and cardiovascular information processing systems in preclinical and in clinical trials; and (9) speeding the development of new drugs by shedding new light on drug-induced brain and heart activity patters.
Consistent with the foregoing objects, and in accordance with the invention as embodied and broadly described herein, apparatus and methods in accordance with the present invention may be used to develop a new drug evaluation protocol which includes the step of obtaining EEG signal data relating to several drug states and using an event resolution imager (ERI) to conduct analysis to determine the best way to distinguish which drug is present or associated with a given time segment or epoch of EEG data. This discrimination capability stems from recognition of learned patterns in the multichannel EEG time series data. Experiments may be performed to find the best discriminant wave-processing sequence. The best wave-processing sequence may then be automated and tested on additional drug-labeled EEG data to determine utility, accuracy and the ability to generalize. Finally, the protocol may be packaged for one-button operation and ease of use, thus making the drug evaluation method of the present invention more economical and efficient.
Particularly, characteristic signals relating to a particular observed entity may be gathered, amplified, processed and recorded. The signals may be divided into time segments or epochs of a selected time period. Each epoch may contain all the signals recorded from the observed entity during that selected time period. Each epoch may be related to a particular event or state of the observed entity that was occurring at the time the signal (signals) contained in the epoch were recorded.
The basic strategy of an event resolution imager (ERI) in accordance with the present invention is to apply several methods of analysis to each epoch to find consistent differences between epochs relating to different events or states and similarities within the epochs related to similar events or states. ERI analysis may consists of three primary processes. Generally, these process include learning, classification and validation.
The learning process may use several waveform analysis techniques including, by way of example and not limitation, time-frequency expansion, feature coherence analysis, principal component analysis and separation analysis. Each epoch may be decomposed into features in an extended phase space representing spatial, time, frequency, phase and interchannel relationships. These features may then be analyzed in detail for characteristics common to each epoch. This analysis may include evaluation of coherence between signals distributed across the four domains of space, time, frequency and phase.
The learning process performs a set of analyses to find features that are most reliably different between epoch types, which is characterized as state separation analysis. This consists of waveform analysis, distribution function analysis and discriminant optimization. Finally, the results of the above analyses are used to generate a set of parameters, components, functions and criteria which best identify epoch type and discriminate between epochs. This information may then be recorded as an interpretation map.
In one presently preferred embodiment of the present invention, a classification process uses the interpretation map to apply the set of analyses and criteria previously determined to be optimal, to the classification of epochs having xe2x80x9cunknownxe2x80x9d events or states corresponding thereto. The classification system analyzes each epoch as an individual event (no averaging) and generates a composite, clean waveform made up of those characteristics or features generated by the analysis.
The classification process may then pass the classification data to an output generator to compile a statistical summary of the results, including the confidence level that each epoch was classified correctly. As appreciated, additional outputs may include calculations of sensitivity, specificity and overall accuracy.
True epoch event or state labels may be bound to analyzed epochs to enable a comparison with epoch classifications generated by the classification system. That is, the actual or true event associated with a particular epoch may provide a key to determine if that epoch has been correctly classified. Accordingly, this may provide a method of testing or validating the accuracy of the ERI. High classification accuracy of epochs that are separate and distinct from the epochs used in the creation of the interpretation map, indicates a valid derived interpretation map.